The present invention concerns a method for the analysis of a biomedical signal such as electrocardiograms (ECG) or other biomedical signals.
There are a variety of biomedical signals, i.e. signals representative of the state or condition of a human or animal, which are obtained invasively or non-invasively by the use of monitoring instrumentation. Typical examples include electrocardiograms (ECG), electroencephalograms (EEG), beat-to-beat blood pressure, the photoplethysmograph (PPG), impedance plethysmograph, respiration rate or impedance pneumogram, and blood oxygen saturation which all have regularly repeating patterns. These signals are typically examined by experts to determine, for example, the state of health of the human or animal, the effect of some therapy, for example, a drug, on the human or animal and so on. Such expert analysis is, however, time consuming and expensive. Considerable efforts have therefore been made over the past few years to provide automated techniques for analysing biomedical signals. Such signals are often noisy, complex and highly variable in time and from individual to individual. Automated analysis is therefore difficult, and the large amount of data which can be generated by signal recordings from even one individual over an extended period can make it impractical to analyse all of the data even using computers with fast processors.
An example of an automated signal analysis method for segmentation of electrocardiograms is disclosed in WO 2005/107587. That document discloses the use of a trained Hidden Markov Model to segment individual heartbeats in an electrocardiogram (ECG). The ECG (also known by the acronym EKG) is an important non-invasive signal which measures the electrical activity of the heart.
Each individual heartbeat is comprised of a number of distinct cardiological stages, which in turn give rise to a set of distinct features in the ECG waveform. These features represent either depolarization (electrical discharging) or repolarization (electrical recharging) of the muscle cells in particular regions of the heart. FIG. 1 shows a human ECG waveform and the associated features. The standard features of the ECG waveform are the P wave, the QRS complex and the T wave. Additionally a small U wave (following the T wave) is occasionally present.
The cardiac cycle begins with the P wave (the start and end points of which are referred to as Pon and Poffset), which corresponds to the period of atrial depolarization in the heart. This is followed by the QRS complex, which is generally the most recognisable feature of an ECG waveform, and corresponds to the period of ventricular depolarization (which masks the atrial repolarization). The start and end points of the QRS complex are referred to as the Qonset and J points. The T wave follows the QRS complex and corresponds to the period of ventricular repolarization. The end point of the T wave is referred to as Toffset and represents the end of the cardiac cycle (presuming the absence of a U wave). By examining the ECG signal in detail it is possible to derive a number of informative measurements from the characteristic ECG waveform. These can then be used to assess cardiac condition and detect changes or potential abnormalities present in the cardiac rhythm.
A particularly important measurement is the “QT interval”, which is defined as the time from the start of the QRS complex to the end of the T wave, i.e. Toffset−Qonset. This timing interval corresponds to the total duration of the electrical activity (both depolarization and repolarization) in the ventricles.
The QT interval is particularly significant since it is a good indicator of certain medical conditions, and it also has to be monitored in volunteers testing new pharmacological substances in clinical trials. In the past such volunteers would have their ECGs monitored and recorded for relatively short times (for example 10 seconds or so) at regular intervals during a clinical trial. For example in connection with the administration of an experimental drug, 10 second ECG recordings might be made on administration of the drug (time point zero), at 30 minutes, one hour, 1.5 hours and so on up to one day later, but typically decreasing in frequency after the first six hours. Typically, as a control, ECG recordings would also be made at the corresponding times on a day when the volunteer is not administered with the drug, and on a day when the volunteer is administered with a placebo. The effect of the drug on the volunteer's heart, for example whether it lengthens the QT interval, and by how much, will be appraised by experts reviewing the short ECG recordings.
More recently, though, concerns that recording short periods of ECG at spaced intervals through the day might miss certain effects has led to continuous recording of all twelve channels (a so-called Holter recording). This hugely increases the amount of ECG data. While manual analysis is possible with short duration recordings at regularly spaced intervals as above, with a continuous Holter recording, analysis of the 24 hours would require of the order of 100,000 beats (60 bpm×60 minutes×24 hours=86,400) to be analysed per channel. This makes existing methods of expert analysis, and indeed many methods of automated analysis, impractical.
An additional problem which occurs in analysis of ECGs is that the QT interval varies with heart rate. So decreased heart rate leads to increased QT interval. The increase and decrease in QT interval associated with heart rate changes is much greater than the change caused by a pharmacological substance. The heart rate also varies periodically with the breathing cycle, and this periodic change also affects the QT interval. Thus values from the QT interval measured from an ECG are usually corrected by dividing the measured QT interval by the cube root or square root of the beat to beat interval (period from one R peak to the next) in seconds. This correction is not, however, particularly accurate.
Further, in order to reduce the effects of heart rate variation it is normal, when assessing ECGs taken on different days, to compare ECGs taken from the same time on each day. Again, though, this is not particularly accurate.
A first aspect of the present invention provides a computer-implemented method for the analysis of a biomedical signal having multiple identifiable time-sequential segments and at least one recognisable periodic signal feature, the method comprising the step of segmenting the signal using a Hidden Markov Model, the model comprising a plurality of states corresponding to successive segments of the signal;
characterised in that:
                the segmentation is performed using only two or more states corresponding to a subset of said multiple identifiable time-sequential segments, the subset forming a part of said signal between a start and an end point,        in that the position of at least one of said start and end points is estimated by reference to said periodic signal feature, and        in that the segmentation is performed only on said part of said signal whose location is based on said estimated position.        
Thus the time-sequential states correspond to states of the Hidden Markov Model. The periodic signal feature is one which is recognisable by a standard procedure.
Thus with this aspect of the invention it is possible to perform segmentation of only part of the biomedical signal using a Hidden Markov Model which includes only two or more states corresponding to that part. This makes the segmentation process much less demanding on the processor and thus quicker.
As indicated in WO 2005/107587, the use of the Hidden Markov Model returns a value for the probability of the segmented waveform being drawn from the distribution of waveforms in the training set, and this can be used as a confidence measure.
The present invention however can provide a further confidence measure based on the difference between predicted value of the location of the required feature of the signal being segmented, and the location detected by use of the Hidden Markov Model segmentation.
The invention may be applied to an electrocardiogram and the part of the signal to be segmented can be any one of the standard cardiological stages of the heart beat, for example the QT interval from Qonset to Toffset. In this case the segmentation can be performed based on an estimated position of Toffset whose position can be estimated initially by detection of a salient feature in the waveform such as the R peak or J point (which is easy to detect and identify once the position of the R peak is known), followed by the use of a relationship between the heart rate and QT interval. This relationship may be the standard relationship or can be estimated by analysis of the electrocardiogram signal itself over a period preceding the beat being segmented.
Preferably for analysis the biomedical signal is divided into successive sections, for example 30 second sections in the case of an ECG, with end portions which temporally overlap (by, for example, 10 seconds in the case of an ECG) with the neighbouring sections. This allows the central portion of each section to be taken after segmentation without having a problem of transient or edge effects at the ends of sections.
A second aspect of the invention provides an enhancement to an existing method of correcting variations in the QT interval in an electrocardiogram (ECG) caused by variations in heart rate, the QT interval being the period from Qonset to Toffset in the electrocardiogram (ECG), the method comprising finding the values of QT interval and corresponding beat to beat time for each of a plurality of beats in a time period spanning the current beat, the beat to beat time being the time period between the R peak of each beat and the R peak of the previous or next beat, performing regression analysis on the QT intervals and corresponding beat to beat times over said time period to calculate the relationship between the QT interval and beat to beat time, using that relationship to correct the QT interval for the current beat, and performing the regression analysis repeatedly based on a new time period spanning the current beat at that time. Thus the relationship between the RR interval and QT interval is constantly adapted for each beat based on a time window spanning that beat. This thus provides an adaptive contemporaneous individual correction of the QT interval. Preferably the time period is in the region of 4 to 6 hours, this being achieved by looking at the ECG for 2 to 3 hours before and after the current beat and performing the QT-RR regression analysis over each successive 4 to 6 hour period, moving forwards in time one or more beats at a time.
A third aspect of the invention utilises the QT-RR relationship mentioned above as an analysis technique in itself. Thus this aspect of the invention provides a method of analysing an electrocardiogram (ecg), comprising finding, for each of a plurality of beats in the electrocardiogram the current value of the slope of the relationship between the QT interval and R-R interval, the QT interval being the period from Qonset to Toffset in a beat in the electrocardiogram (ecg) and the R-R interval being the time period between the R peak of the corresponding beat and the R peak of a neighbouring beat, each current value of the slope being obtained by performing regression analysis on the values of QT intervals and corresponding R-R intervals for beats in a respective time period spanning each of said plurality of beats, the method further comprising displaying the calculated current slope values for each of said plurality of beats, thereby to display variations in said slope through the duration of the Holter recording.
It is found, for example, that the slope of the QT-RR relationship can vary depending on the state of the individual. Certain pharmacological substances, for example, can cause the slope to vary and thus this aspect of the invention provides a way of detecting and monitoring changes in cardiac activity.
Preferably, as before, the respective time periods are of 4 to 6 hours in duration, although different time periods can be adopted in different applications.
Another aspect of the invention provides a method of analysing two biomedical signals of a patient taken on different days, comprising selecting a portion of each of said biomedical signals obtained during the same time period on each of said different days, defining a similarity metric to measure the similarity of subsections of the two portions, using the similarity metric to select a subsection of each of the two portions which are similar, and performing comparative analysis on said selected subsections.
Thus with this aspect of the invention the subsections selected will not necessarily be from exactly corresponding times of day. Instead time windows are defined at the same time of day for the two ECGs and within those time windows subsections which are similar to each other are selected for further comparative analysis.
The similarity metric may measure similarity on the basis of one or more features of the signal, while the comparative analysis is to determine differences in a different feature of the signal. Preferably the subsections are those that have the maximum similarity within the time window.
In the case of an ECG the comparative analysis may be comparing the QT intervals, while the similarity metric measures the heart rate or shape of the waveform. Thus, in the case of an ECG the QT intervals are compared for heart beats which are similar in shape or for heart beats deriving from a portion of the ECG where the heart rate is the same (or as similar a possible).
In the case of an ECG the subsection selected from each of the two portions can be several of the most similar individual beats, e.g. three pairs of beats, or alternatively the comparative analysis can proceed on the basis of an average (e.g. mean or median) value of a feature over the selected subsections.
The present invention can be implemented in the form of a computer program for processing digitised biomedical signals, and the invention extends to such a program, to a storage medium storing such a program and to a computer system programmed to carry out the method.